1. Field
The invention disclosed and claimed herein generally pertains to a system and method for managing enterprise data quality, which makes use of targeted subject matter experts (SMEs) to collectively validate and enhance selected data elements. More particularly, the invention pertains to a system and method of the above type, wherein SMEs are identified by searching information sources such as existing ticket logs and server access logs that are related to the selected data sets or data elements.
2. Description of the Related Art
In regard to data quality, ticketing systems, data stores, and warehouses continue to evolve and mature. This is important, because business insights and problem resolution processes require careful data quality assessment in order to build credibility with stakeholders and to efficiently resolve incidents. However, resources of the above type are not fast enough for many businesses. Also, the quality of a set of data is only as good as the sources which provide it. For example, when humans are entering data, errors can be made due to time pressure, distractions, or for other reasons. Servers or other system components may nominally contain the same set of data, but an update made to the data set in one of the servers may not be made to the data set in the other. Furthermore, data stores and warehouses may use different format and conventions for same data elements leading to further inconsistencies and conflicting results. Also, data quality can vary by request, and with the corresponding data source.
At present, certain tools or systems are available to keep track of the system administrators who have accessed particular servers. Monitoring tools can indicate what was performed, such as the commands that were executed and the times thereof. System logs can be used to determine who, what server, when, and what role was used on each access. Problem tickets may be used to provide context, such as the reported problem, the related account, and the affected server. However, at present there are generally no systems available to automatically and systematically ensure quality of enterprise data that comes from both human and digital sources.